The Internet of Things (IoT) is seen as the most viable solution for real-time monitoring applications. But the faults occurring at the perception layer are prone to misleading the data driven system and consume higher bandwidth and power. Thus, the goal of this effort is to provide an edge deployable sensor-fault detection and identification algorithm to reduce the detection, identification, and repair time, save network bandwidth and decrease the computational stress over the Cloud. Towards this, an integrated algorithm is formulated to detect fault at source and to identify the root cause element(s), based on Random Forest (RF) and Fault Tree Analysis (FTA). The RF classifier is employed to detect the fault, while the FTA is utilized to identify the source. A Methane (CH4) sensing application is used as a case-study to test the proposed system in practice. We used data from a healthy CH4 sensing node, which was injected with different forms of faults, such as sensor module faults, processor module faults and communication module faults, to assess the proposed model’s performance. The proposed integrated algorithm provides better algorithm-complexity, execution time and accuracy when compared to FTA or standalone classifiers such as RF, Support Vector Machine (SVM) or K-nearest Neighbor (KNN). Metrics such as Accuracy, True Positive Rate (TPR), Matthews Correlation Coefficient (MCC), False Negative Rate (FNR), Precision and F1-score are used to rank the proposed methodology. From the field experiment, RF produced 97.27% accuracy and outperformed both SVM and KNN. Also, the suggested integrated methodology’s experimental findings demonstrated a 27.73% reduced execution time with correct fault-source and less computational resource, compared to traditional FTA-detection methodology.
Aim: The present study aims at screening the phytochemical components and evaluates the antioxidant, anti-bacterial, and anti-inflammatory activity of fruit of Piper schmidtii, an endemic plant species from The Nilgiris, Tamil Nadu. Materials and Method: The different polar solvents such as petroleum ether, chloroform, ethyl acetate, methanol, and water were used and extraction was carried out using the soxhlet apparatus. The extracts were screened for qualitative and quantitative phytochemical analysis. The extracts of P. schmidtii were also subjected to in vitro-antioxidant activity by DPPH assay, Phosphomolybdenum assay, Ferric reducing antioxidant power (FRAP), superoxide radical scavenging activity, and reducing power assay. Results: Among all the extracts, methanol extract exhibited the maximum amount of phenolics (731.91 mg GAE/g extract), tannin (726.6 milligrams of Gallic acid equivalent/g extract), and ethyl acetate extract depicted the maximum quantity of flavonoids (698.17 mg QE/g extract). Methanol extract of P. schmidtii revealed the higher antioxidant activity in all the assays with IC50 values of 15.19 μg/ml (DPPH), 135.67 mg AAE/g (Phosphomolybdenum assay), 380.98 mM Fe/mg (FRAP), 60.94% (Superoxide) and higher reducing power was depicted in the ethyl acetate extract, respectively. Further anti-bacterial activity revealed that the methanol extract shows highest inhibitory activity against the tested bacterial pathogens. The methanol extract showed high degree of inhibition (71.24%) in anti-inflammatory assay. Conclusion: Thus, the result support that P. schmidtii is a potential source of natural antioxidant that can inhibit bacterial growth and subside inflammation.
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